Open Access

ARTICLE

Denoising Medical Images Using Deep Learning in IoT Environment

by Sujeet More¹, Jimmy Singla¹, Oh-Young Song^2,*, Usman Tariq³, Sharaf Malebary⁴

1 School of Computer Science and Engineering, Lovely Professional University, Jalandhar, India
2 Software Department, Sejong University, Seoul, Korea
3 College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
4 Department of Information Technology, Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Jeddah, 21911, Saudi Arabia

* Corresponding Author: Oh-Young Song. Email:

(This article belongs to the Special Issue: Intelligent Big Data Management and Machine Learning Techniques for IoT-Enabled Pervasive Computing)

Computers, Materials & Continua 2021, 69(3), 3127-3143. https://doi.org/10.32604/cmc.2021.018230

Received 01 March 2021; Accepted 02 April 2021; Issue published 24 August 2021

Abstract

Medical Resonance Imaging (MRI) is a noninvasive, nonradioactive, and meticulous diagnostic modality capability in the field of medical imaging. However, the efficiency of MR image reconstruction is affected by its bulky image sets and slow process implementation. Therefore, to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network (SANR_CNN) for eliminating noise and improving the MR image reconstruction quality. The proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality, and SARN algorithm is used for building a dictionary learning technique for denoising large image datasets. The proposed SANR_CNN model also preserves the details and edges in the image during reconstruction. An experiment was conducted to analyze the performance of SANR_CNN in a few existing models in regard with peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). The proposed SANR_CNN model achieved higher PSNR, SSIM, and MSE efficiency than the other noise removal techniques. The proposed architecture also provides transmission of these denoised medical images through secured IoT architecture.

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Keywords

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